Why Do Some LLMs Come in GGUF Format?

What does it mean when an LLM model has “GGUF” in its name? 

If you’ve been exploring large language models (LLMs), you might have come across GGUF files. But what exactly does that mean?

What is GGUF?

It is a file format designed for quantized LLMs.

GGUF stands for GPT-Generated Unified Format.

Before GGUF, there were multiple similar formats:

• GGML
• GGMF
• GGJT

GGUF simplifies things by unifying them under one standard.

Why does it matter?

Quantization helps models run more efficiently by reducing precision.

Examples of GGUF quantization levels:

A model like Bielik-11B-v.23-Instruct may offer different GGUF versions, such as:

• Q4_K_M
• Q5_K_M
• Q6_K
• Q8_0
• IQ4_XS (experimental)

Each of these represents a different tradeoff between speed, memory usage, and accuracy.

Useful mention:

If you run `ollama run llama3.2`, it will automatically download the Q4_K_M quantized version.

What do you think?

• Have you experimented with GGUF models?
• What’s your experience with different quantization levels?

Drop your thoughts in the comments! 👇

If you're also learning about LLMs, feel free to drop me a DM! 🙂

You can also check out posts about:

• How to Set Up BIELIK AI with vLLM and GGUF (Easy Guide)
• Why BIELIK AI Chooses FP16—The Secret to Faster Models!
• How I Made BIELIK 11B v2.3 Run on Half the Memory. Quantized Models 

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